Overview

Dataset statistics

Number of variables5
Number of observations35041
Missing cells0
Missing cells (%)0.0%
Total size in memory1.3 MiB
Average record size in memory40.0 B

Variable types

DateTime1
TimeSeries4

Timeseries statistics

Number of series4
Time series length35041
Starting point0
Ending point35040
Period1
2023-10-30T01:25:12.677126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:13.073760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Alerts

BE is non stationaryNon stationary
DE is non stationaryNon stationary
FR is non stationaryNon stationary
NP is non stationaryNon stationary
BE is seasonalSeasonal
DE is seasonalSeasonal
FR is seasonalSeasonal
NP is seasonalSeasonal
Date has unique valuesUnique

Reproduction

Analysis started2023-10-30 01:24:36.797942
Analysis finished2023-10-30 01:25:12.434375
Duration35.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Date
Date

UNIQUE 

Distinct35041
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size273.9 KiB
Minimum2013-01-01 00:00:00
Maximum2016-12-31 00:00:00
2023-10-30T01:25:13.589500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:14.020097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BE
Numeric time series

NON STATIONARY  SEASONAL 

Distinct7093
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.37932936
Minimum-200
Maximum696.02
Zeros1
Zeros (%)< 0.1%
Memory size273.9 KiB
2023-10-30T01:25:14.395265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile16.77
Q130.93
median41
Q352
95-th percentile69.46
Maximum696.02
Range896.02
Interquartile range (IQR)21.07

Descriptive statistics

Standard deviation20.09964039
Coefficient of variation (CV)0.4742793408
Kurtosis124.6351239
Mean42.37932936
Median Absolute Deviation (MAD)10.47
Skewness5.710998491
Sum1485014.08
Variance403.9955439
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.935337175 × 10-27
2023-10-30T01:25:14.887899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-30T01:25:16.578963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-10-30T01:25:16.862953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
50 217
 
0.6%
40 204
 
0.6%
45 194
 
0.6%
44.94 186
 
0.5%
39.94 166
 
0.5%
60 163
 
0.5%
42.44 158
 
0.5%
55 152
 
0.4%
35 135
 
0.4%
47.44 124
 
0.4%
Other values (7083) 33342
95.2%
ValueCountFrequency (%)
-200 3
< 0.1%
-154.02 1
 
< 0.1%
-100.03 1
 
< 0.1%
-100 1
 
< 0.1%
-58.67 1
 
< 0.1%
ValueCountFrequency (%)
696.02 1
 
< 0.1%
678.31 1
 
< 0.1%
579.53 1
 
< 0.1%
448.7 5
< 0.1%
399 2
 
< 0.1%
2023-10-30T01:25:15.646074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

DE
Numeric time series

NON STATIONARY  SEASONAL 

Distinct6360
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.78240347
Minimum-130.09
Maximum130.27
Zeros5
Zeros (%)< 0.1%
Memory size273.9 KiB
2023-10-30T01:25:17.331131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-130.09
5-th percentile11.93
Q124.94
median31.61
Q340.01
95-th percentile57.36
Maximum130.27
Range260.36
Interquartile range (IQR)15.07

Descriptive statistics

Standard deviation14.06566308
Coefficient of variation (CV)0.429061374
Kurtosis4.252154989
Mean32.78240347
Median Absolute Deviation (MAD)7.5
Skewness0.01069114626
Sum1148728.2
Variance197.8428778
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.493114461 × 10-30
2023-10-30T01:25:18.000487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-30T01:25:20.423339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-10-30T01:25:20.700105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
34.94 52
 
0.1%
30 51
 
0.1%
35 47
 
0.1%
30.03 46
 
0.1%
40 42
 
0.1%
30.01 40
 
0.1%
28.08 36
 
0.1%
34.95 35
 
0.1%
30.06 35
 
0.1%
29 35
 
0.1%
Other values (6350) 34622
98.8%
ValueCountFrequency (%)
-130.09 1
< 0.1%
-100.06 1
< 0.1%
-100.03 1
< 0.1%
-100 1
< 0.1%
-82.06 1
< 0.1%
ValueCountFrequency (%)
130.27 1
< 0.1%
120.16 1
< 0.1%
116.53 1
< 0.1%
114.32 1
< 0.1%
110.24 1
< 0.1%
2023-10-30T01:25:19.540638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

FR
Numeric time series

NON STATIONARY  SEASONAL 

Distinct8071
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.26043101
Minimum-200
Maximum874.01
Zeros1
Zeros (%)< 0.1%
Memory size273.9 KiB
2023-10-30T01:25:21.260768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile13.6
Q126.4
median36.76
Q348.44
95-th percentile66.37
Maximum874.01
Range1074.01
Interquartile range (IQR)22.04

Descriptive statistics

Standard deviation18.79487027
Coefficient of variation (CV)0.4912351946
Kurtosis311.3151931
Mean38.26043101
Median Absolute Deviation (MAD)10.97
Skewness7.714939884
Sum1340683.763
Variance353.2471485
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.348024523 × 10-23
2023-10-30T01:25:21.902282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-30T01:25:23.704219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-10-30T01:25:24.007438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
50 82
 
0.2%
40 66
 
0.2%
45 62
 
0.2%
35 55
 
0.2%
60 45
 
0.1%
44 44
 
0.1%
30 43
 
0.1%
34.94 40
 
0.1%
55 40
 
0.1%
44.94 40
 
0.1%
Other values (8061) 34524
98.5%
ValueCountFrequency (%)
-200 3
< 0.1%
-154.02 1
 
< 0.1%
-100.03 1
 
< 0.1%
-100 1
 
< 0.1%
-58.67 1
 
< 0.1%
ValueCountFrequency (%)
874.01 1
< 0.1%
850.07 1
< 0.1%
829.79 1
< 0.1%
429.46 1
< 0.1%
267.65 1
< 0.1%
2023-10-30T01:25:22.806575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

NP
Numeric time series

NON STATIONARY  SEASONAL 

Distinct4816
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.90000927
Minimum1.14
Maximum199.97
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-10-30T01:25:24.512842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile13.03
Q123.25
median28.61
Q334.71
95-th percentile43.78
Maximum199.97
Range198.83
Interquartile range (IQR)11.46

Descriptive statistics

Standard deviation9.643763669
Coefficient of variation (CV)0.3336941375
Kurtosis12.51893558
Mean28.90000927
Median Absolute Deviation (MAD)5.7
Skewness1.105921821
Sum1012685.225
Variance93.00217771
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.262429726 × 10-8
2023-10-30T01:25:25.089556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-10-30T01:25:27.250311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-10-30T01:25:27.557636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
25.03 32
 
0.1%
23.91 32
 
0.1%
32.25 30
 
0.1%
30.92 30
 
0.1%
25.05 29
 
0.1%
25.97 29
 
0.1%
26.13 28
 
0.1%
24.54 28
 
0.1%
25.1 28
 
0.1%
28.02 28
 
0.1%
Other values (4806) 34747
99.2%
ValueCountFrequency (%)
1.14 1
< 0.1%
1.15 1
< 0.1%
1.18 1
< 0.1%
1.27 1
< 0.1%
1.38 1
< 0.1%
ValueCountFrequency (%)
199.97 1
< 0.1%
199.94 1
< 0.1%
168.64 1
< 0.1%
160.03 1
< 0.1%
160 1
< 0.1%
2023-10-30T01:25:26.192895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ACF and PACF

Interactions

2023-10-30T01:25:10.744185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:07.343689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:08.406023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:09.632365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:11.007153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:07.595293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:08.689700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:09.904506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:11.280510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:07.846803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:09.012935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:10.195246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:11.579890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:08.123523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:09.309582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-30T01:25:10.458254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-30T01:25:27.931547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
BEDEFRNP
BE1.0000.7380.8580.523
DE0.7381.0000.7860.544
FR0.8580.7861.0000.513
NP0.5230.5440.5131.000

Missing values

2023-10-30T01:25:12.001090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-30T01:25:12.310937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.